At present, all mobile application stores will push some mobile applications to a user when the user downloads or browses an application, so as to recommend mobile applications to the user; the push method is to take statistics of the relevance between mobile applications according to a user history log, and then generate a recommendation result according to the relevance and using recommendation algorithms such as neighbor and collaborative filtering; therefore, the relevance between mobile applications is taken as a recommendation basis to recommend mobile applications in the prior art.
Therefore, at present, the following problems exist in the recommendation method of mobile applications:
1. since the relevance between mobile applications is always taken as a recommendation basis, this makes the contents of the recommended mobile applications too similar to each other, and a variety of mobile applications cannot be recommended to the user, and thus the needs of the user for mobile applications cannot be stimulated.
2. Since newly added mobile applications do not have a user history log, the statistics of the relevance between the newly added mobile applications and other mobile applications cannot be taken; therefore, the newly added mobile applications cannot be recommended to the user when the user views or downloads mobile applications, and the cold start problem of the newly added mobile applications cannot be solved.